To address the pressure on risk control systems, this paper builds an intelligent audit and financial risk prediction platform based on adaptive reinforcement learning (ARL). This platform achieves dynamic risk monitoring and accurate prediction through problem modeling, strategy optimization, and multi-source data fusion. First, a financial risk state space model combining time series and nonlinear features is established. Second, a multidimensional reward function tailored to the audit task is constructed to balance prediction accuracy with anomaly detection recall. An adaptive reinforcement learning framework is then used to iteratively train historical transaction data, audit records, and macroeconomic indicators to dynamically update the risk prediction strategy. A feature extraction module based on a multi-layer neural network is then designed to effectively characterize high-order relationships between potential risk factors. Finally, a transfer learning mechanism is introduced to enhance cross-industry and cross-scenario generalization capabilities. In a three-year test using actual business data from a large bank, this platform achieves a 94.2% accuracy rate in risk event prediction, with an average recall rate of 78.5%. This approach offers significant advantages in improving the level of intelligent auditing and providing proactive early warning of financial risks.
Lyu et al. (Thu,) studied this question.